A Comperative Evaluation of Active Appearance Model Algorithms

An Active Appearance Model (AAM) allows complex models of shape and appearance to be matched to new images rapidly. An AAM contains a sta-tistical model of the shape and grey-level appearance of an object of interest The associated search algorithm exploits the locally linear relationship be-tween model parameter displacements and the residual errors between model instance and image. This relationship can be learnt during a training phase. To match to an image we measure the current residuals and use the model to predict changes to the current parameters. The algorithm converges in a few iterations. In this paper we describe variations of the basic algorithm aimed at improving the speed and robustness of search. These include sub-sampling and using image residuals to drive the shape rather than full appear-ance model. We show examples of search and give the results of experiments comparing the performance of the different algorithms.